MENU ▼
Read by QxMD icon Read
search
OPEN IN READ APP
JOURNAL ARTICLE

Personalizing Second-Line Type 2 Diabetes Treatment Selection: Combining Network Meta-analysis, Individualized Risk, and Patient Preferences for Unified Decision Support

Sung Eun Choi, Seth A Berkowitz, John S Yudkin, Huseyin Naci, Sanjay Basu
Medical Decision Making: An International Journal of the Society for Medical Decision Making 2019 February 15, : 272989X19829735
30767632

BACKGROUND: Personalizing medical treatment often requires practitioners to compare multiple treatment options, assess a patient's unique risk and benefit from each option, and elicit a patient's preferences around treatment. We integrated these 3 considerations into a decision-modeling framework for the selection of second-line glycemic therapy for type 2 diabetes.

METHODS: Based on multicriteria decision analysis, we developed a unified treatment decision support tool accounting for 3 factors: patient preferences, disease outcomes, and medication efficacy and safety profiles. By standardizing and multiplying these 3 factors, we calculated the ranking score for each medication. This approach was applied to determining second-line glycemic therapy by integrating 1) treatment efficacy and side-effect data from a network meta-analysis of 301 randomized trials ( N = 219,277), 2) validated risk equations for type 2 diabetes complications, and 3) patient preferences around treatment (e.g., to avoid daily glucose testing). Data from participants with type 2 diabetes in the U.S. National Health and Nutrition Examination Survey (NHANES 2003-2014, N = 1107) were used to explore variations in treatment recommendations and associated quality-adjusted life-years given different patient features.

RESULTS: Patients at the highest microvascular disease risk had glucagon-like peptide 1 agonists or basal insulin recommended as top choices, whereas those wanting to avoid an injected medication or daily glucose testing had sodium-glucose linked transporter 2 or dipeptidyl peptidase 4 inhibitors commonly recommended, and those with major cost concerns had sulfonylureas commonly recommended. By converting from the most common sulfonylurea treatment to the model-recommended treatment, NHANES participants were expected to save an average of 0.036 quality-adjusted life-years per person (about a half month) from 10 years of treatment.

CONCLUSIONS: Models can help integrate meta-analytic treatment effect estimates with individualized risk calculations and preferences, to aid personalized treatment selection.

Comments

You need to log in or sign up for an account to be able to comment.

No comments yet, be the first to post one!

Related Papers

Available on the App Store

Available on the Play Store
Remove bar
Read by QxMD icon Read
30767632
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"